foobar

README.md

@@ -2,8 +2,10 @@
 This is the repository for the LinkedIn Learning course `Hands-On AI: Building and Deploying LLM-Powered Apps`. The full course is available from [LinkedIn Learning][lil-course-url].
 
 _See the readme file in the main branch for updated instructions and information._
-## Lab2: Adding LLM to Chainlit App
-Now we have a web interface working, we will now add an LLM to our Chainlit app to have our simplified version of ChatGPT. We will be using [Langchain](https://python.langchain.com/docs/get_started/introduction) as the framework for this course. It provides easy abstractions and a wide varieties of data connectors and interfaces for everything LLM app development.
+## Lab3: Enabling Load PDF to Chainlit App
+Building on top of the current simplified version of ChatGPT using Chainlit, we now going to add loading PDF capabilities into the application.
+
+ NowNow we have a web interface working, we will now add an LLM to our Chainlit app to have our simplified version of ChatGPT. We will be using [Langchain](https://python.langchain.com/docs/get_started/introduction) as the framework for this course. It provides easy abstractions and a wide varieties of data connectors and interfaces for everything LLM app development.
 
 In this lab, we will be adding an Chat LLM to our Chainlit app using Langchain.
 

app/app.py

@@ -4,30 +4,14 @@ from langchain.prompts import ChatPromptTemplate
 from langchain.schema import StrOutputParser
 from langchain.chains import LLMChain
 
+
 @cl.on_chat_start
 async def on_chat_start():
-    ##########################################################################
-    # Exercise 1a:
-    # Our Chainlit app should initialize the LLM chat via Langchain at the
-    # start of a chat session.
-    # 
-    # First, we need to choose an LLM from OpenAI's list of models. Remember
-    # to set streaming=True for streaming tokens
-    ##########################################################################
     model = ChatOpenAI(
         model="gpt-3.5-turbo-1106",
         streaming=True
     )
 
-    ##########################################################################
-    # Exercise 1b:
-    # Next, we will need to set the prompt template for chat. Prompt templates
-    # is how we set prompts and then inject informations into the prompt.
-    # 
-    # Please create the prompt template using ChatPromptTemplate. Use variable
-    # name "question" as the variable in the template.
-    # Refer to the documentation listed in the README.md file for reference.
-    ##########################################################################
     prompt = ChatPromptTemplate.from_messages(
         [
             (
@@ -40,12 +24,6 @@ async def on_chat_start():
             ),
         ]
     )
-    ##########################################################################
-    # Exercise 1c:
-    # Now we have model and prompt, let's build our Chain. A Chain is one or a
-    # series of LLM calls.We will use the default StrOutputParser to parse the
-    # LLM outputs.
-    ##########################################################################
     chain = LLMChain(llm=model, prompt=prompt, output_parser=StrOutputParser())
 
     # We are saving the chain in user_session, so we do not have to rebuild
@@ -59,12 +37,6 @@ async def main(message: cl.Message):
     # Let's load the chain from user_session
     chain = cl.user_session.get("chain")  # type: LLMChain
 
-    ##########################################################################
-    # Exercise 1d:
-    # Everytime we receive a new user message, we will get the chain from 
-    # user_session. We will run the chain with user's question and return LLM
-    # response to the user.
-    ##########################################################################
     response = await chain.arun(
         question=message.content, callbacks=[cl.LangchainCallbackHandler()]
     )